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Lately, researchers have widely centered around renewable energy assets, for example, wind control and solar units to decrease the utilization of fossil fuels as the fundamental wellspring of ecological natural contaminations. One of the fundamental difficulties for the generation of wind and solar energies is that they frequently including the uncertainty because of the stochastic natures of wind speeds and solar radiation. Along these lines, the current uncertainty to assets the wind and solar units and necessary to assess the arranging strategy of distribution systems for having a dependable execution. To display the uncertainty related along with the wind and solar power, the point estimate method (PEM) is utilized. Weibull and Beta distributions are utilized to deal with uncertain information factors. These fundamental goals present work is to minimize the generation cost of the framework is enhanced dependent on the butterfly optimization algorithm (BOA). Four case test frameworks viewed as it is discovered that the proposed strategy gives better arrangement as far as execution time and normal cost viability. The recreation results demonstrate that the entrance of sustainable energy source builds, the generation cost diminishes. The outcomes acquired along with the butterfly optimization algorithm contrasted and another one understood strategy. Also, the precise distribution of generation cost.

In order to reduce the impacts caused by large-scale renewable energy resources accessing the utility grid, the micro-energy grid system, as a natural extension of the microgrid in the energy internet era, is proposed and developed to provide a new solution for the optimal utilization of renewable energy resources. In this paper, a multi-energy integrated micro-energy system is proposed which contains wind, PV, bedrock energy storage, magnetic levitation electric refrigeration, solid oxide fuel cell, solar thermal collector, energy storage, and V2G technologies, and detailed models of the energy generation/conversion/storage devices are formulated. Besides this, the uncertainties of renewable energy resources and cold/heat/electricity loads are considered, and the optimal dispatch problem of the micro-energy system is established from day-ahead and real-time time scales based on a model predictive control method. The day-ahead optimal scheduling aims at economic optimization and guides real-time scheduling, and real-time scheduling utilizes rolling optimization and a feedback correction mechanism to effectively correct the deviation of renewable energy generations and loads at a real-time horizon, which improves the optimization control accuracy, follows the day-ahead dispatch plan, and ensures the economics of real-time scheduling at the same time.

Hydrogen can be produced from varieties of feedstock. Its ability to reduce the intermittency of renewable energy, along with its versatility in terms of producing or storing energy make it the perfect complement in a system in which renewable energies are the key players. Under the background of integrated energy system (IES), the hydrogen storage system and hydrogen supply chain equipment will be more widely used. This paper studies configuration of hydrogen storage system and hydrogen supply chain equipment in the regional integrated energy system, aiming at reducing wind curtailment and total cost. Firstly, a regional integrated energy system model with hydrogen storage system and hydrogen supply chain equipment is established. Then, aiming at economy, the optimal configuration model of hydrogen storage system and hydrogen supply chain equipment is established. Finally, the effectiveness of the model is verified by a practical example, and the MATLAB toolbox YALMIP with the CPLEX solver is used to conduct the IES configuration planning.

In order to solve the problem of integrated energy/thermal energy storage configuration, a regional integrated energy system power/thermal energy storage configuration method based on wind power prediction is proposed. First of all, a dynamic neural network prediction model of wind power prediction is constructed to effectively predict the power of wind farms in regional integrated energy systems. Then, the behavior of charging and releasing energy from regional integrated energy system is analyzed, the connection between power storage, heat storage and regional wind farm power is established, and the electric coupling relationship is considered to form a joint optimization configuration model of electric/thermal energy storage. Finally, select the actual data of a certain place, set up three scenarios, the prediction results are substituted into the optimization model to calculate the power storage and heat storage configuration, and the comparison verifies that the wind power prediction under this method has a good effect, which is conducive to the optimal allocation of energy storage resources and reduce costs.

Energy Storage (ES) has become an important supporting technology for utilization in large-scale centralized energy generation and DG. And Energy Storage System (ESS) will become the key equipment to combine electric energy and other energy. ESS breaks the unsynchronized of energy generation and consumption, then make different kinds of energies can translatable in time scale and space scale. This paper do a review of energy storage system study.it include the classification and Characteristics of Energy Storage System, the energy storage technology in new energy generation, introducing hybrid energy storage as a typical ESS, and doing a review of ESS business model.